Recently, boosting has come to be used widely in object-detection applications because of its impressive performance in both speed and accuracy. However, learning weak classifier...
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
We derive a number of well known deterministic latent variable models such as PCA, ICA, EPCA, NMF and PLSA as variational EM approximations with point posteriors. We show that the...
Max Welling, Chaitanya Chemudugunta, Nathan Sutter
This paper describes a watermarking method to directly embed information into a CABAC entropy coded H.264/AVC stream. In theory, modification of a part of an arithmetically coded ...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...